The present invention relates to wireless communication receivers, and particularly relates to scaling parameter estimation in Generalized RAKE receivers.
With multipath propagation, a receiver receives multiple images of the transmitted signal at different delays corresponding to the different propagation paths. “Standard” RAKE receivers improve reception performance in multipath environments by time aligning each of one or more RAKE fingers with a corresponding one of the propagation path delays. Each RAKE finger outputs despread traffic values from the signal image corresponding to that propagation path delay, and a combining circuit forms a combined signal based on combining the finger outputs according to a set of combining weights. The combining weight assigned to each finger generally is computed as the complex conjugate of the channel tap calculated for that finger's delay path.
If the received signal impairments are uncorrelated across the RAKE fingers, the standard RAKE receiver solution is optimal. However, the presence of correlated impairments in the received signal degrades the performance of the standard RAKE receiver solution, and the performance degradation can be substantial.
For various reasons, such as the use of higher transmission bandwidths for increased data rates, receivers intended for use in the evolving wireless communication networks are more likely to “see” significant levels of colored noise and other correlated impairments in their received signals. As such, the standard RAKE receiver often is not a suitable candidate architecture for receivers intended to operate in such circumstances.
A newer approach, often termed a “Generalized” RAKE (G-RAKE) receiver, improves reception performance in dispersive channel environments by offering a combination of interference cancellation and channel equalization. To gain these improvements over the standard RAKE receiver architecture, the G-RAKE receiver modifies its signal processing operations in a number of significant ways. First, one or more of its fingers are placed off-path, i.e., offset from the path delays measured for the received signal. Second, its combining weights are not strictly channel tap conjugates. Rather, the combining weights are based at least in part on impairment correlation estimates, which allow the combining process to reduce correlated interference in the combined signal.
One type of G-RAKE recognizes that the impairment correlations can be decomposed into constituent elements, and that the underlying “structure” of these constituent elements can be modeled. As used herein, the term “impairment” has a broad definition that includes, but is not limited to, one or more of the following items: adjacent system interference, self and multi-user interference, and noise. Because modeled terms are used in their impairment correlation estimations, these types of G-RAKE receivers are broadly referred to as “parametric” G-RAKE receivers. As an example, the impairment correlations measured for the received signal may be expressed as the sum of modeled interference correlations as scaled by a corresponding model fitting parameter and modeled noise correlations as scaled by a corresponding model fitting parameter. Because the structures of the modeled terms are known, and the short-term impairment correlations may be measured from a set of despread pilot values, for example, the impairment correlation estimation task is reduced to the determination of the appropriate model fitting parameters, which are also referred to as scaling parameters.
However, challenges remain in parametric G-RAKE processing. More particularly, certain conditions make it difficult to maintain appropriately updated scaling parameters. In particular, the scaling parameters depend on the total energy per chip period of the signal, Ec, and on the white noise or simply noise power (thermal noise plus other interference), N0. These values change rapidly under certain circumstances, making it difficult for parametric G-RAKE receivers to maintain appropriately updated scaling parameters. Examples include the varying transmission conditions, e.g., widely varying transmission powers, associated with scheduled and non-scheduled users on high-rate shared data channels, such as the High Speed Downlink Shared Channel (HS-DSCH) for High Speed Downlink Packet Access (HSDPA) introduced in Release 5 of the Wideband CDMA (WCDMA) standards. Further, the introduction of the Enhanced Up-Link (EUL) in Release 6 of the WCDMA standards, which also includes strict user scheduling, portends complications for parameter estimation in G-RAKE receiver applications.